About the Project

Mission

Lane Vector exists to establish the mathematical foundations of behavioral and temporal dynamics—applying the rigor of classical mechanics and computational physics to domains that have, until now, operated on heuristics and approximation.

Origin

Lane Vector began with an observation that should have been obvious: observable signals have direction and trajectory. For decades, many industries treated behavioral and temporal data as scalar—counts, static scores, single metrics. This is the equivalent of measuring the speed of a particle while ignoring its path. Michael Brandon Lane recognized that the dynamics of such systems bear a structural resemblance to problems already solved in physics: multi-scale temporal stiffness (combustion chemistry), mass action kinetics (chemical equilibrium), and least-action principles (Lagrangian mechanics). The question was not whether these frameworks could apply, but why no one had formalized the application. Operating through Golden Goose Tools in Tennessee—with proximity to Oak Ridge National Laboratory—the project brings national-laboratory-grade mathematics to domains underserved by the physics community.

Vision

We are working toward a world where behavioral and temporal dynamics are a solved physics problem. Lane Vector's long-term research agenda aims to establish a complete, equation-driven theory—validated through the same standards of reproducibility and falsifiability that govern the physical sciences. From heuristics to Hamiltonians.

Methodology overview

The research pipeline combines time-series data with a vector taxonomy: we compute velocity and acceleration of signals (observables), apply trend detection and quasi-steady-state analysis, and produce deliverables such as audit reports, Vector Acquisition Infrastructure (VAI) plans, and kinematic packs. In one instantiation of the framework—for example in discovery and intent—we use Linguistic Regression to compute a Conversion Probability Score (CPS), distinguishing signal from commercial and noise classes.